Describe attribute-oriented induction for data characterization.

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ATTRIBUTE ORIENTED INDUCTION FOR DATA CHARACTERIZATION 

The Attribute-Oriented Induction (AOI) approach to data generalization and summarization-based characterization was first proposed in 1989, a few years before the introduction of the data cub approach. The data cube approach can be considered as a data warehouse-based, pre-computational-oriented, materialized approach. It performs offline aggregation before an OLAP or data mining query is submitted for processing. On the other hand, the attribute-oriented induction approach, at least in its initial proposal, is a relational database query-oriented, generalized-based, online data analysis technique. However, there is no inherent barrier distinguishing the two approaches based on online aggregation versus offline pre-computation.

Basic Principles of Attribute Oriented Induction

 A set of basic principles for the attribute-oriented induction in relational databases is summarized as follows:-

1. follows Data focusing: Analyzing task-relevant data, including dimensions, and the result is the initial relation.

2. Attribute-removal: To remove attribute A if there is a large set of distinct values for A but either

a.There is no generalization operator on A, or

b. A's higher-level concepts are expressed in terms of other attributes. 

3. Attribute-generalization: If there is a large set of distinct values for A, and there exists a set of generalization operators on A, then select an operator and generalize A. 3.

4. Attribute-threshold control: Typical 2-8, specified/default.

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